Predict the Best Variants of Cutting in Turning Process Using Genetic Algorithm Technique

Al-Duroobi, Ahmed A. A.; Ibraheem, Marwa Qasim; Obaeed, Nareen Hafidh

doi:10.1109/icets.2018.8724617

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GA is considered to be an unconventional technique for multi-parameter optimization [20]. Genetic algorithms create generations iteratively as well as abstracting the issue from area to be an estimated population of individuals and attempting to discover the most suitable individual [21].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

Obaeed,

Hamdan

2024

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

The production of functional parts, including those employed by the biomedical industry has been achieved a promising candidate in fused deposition modelling (FDM). The essential properties of these biomedical parts which manufactured by additive manufacturing as compared to some other conventional manufacturing processes depend on structural and process parameters rather than material properties alone. Regarding to the evaluation the flexural strength of medical-grade, polymethylmethacrylate (PMMA) has been received relatively very little investigation to date. PMMA is a biocompatible filament that be used in manufacturing of patient-specific implants such as dental prosthesis and orthopaedic implants. The proposed work explores the effect of three process parameters that vary with respect of three levels on the flexural strength. These levels can be specified by layer height (120, 200, 280 µm), infill density (40, 65, 90%) and skewing angle (0º, 45º, 90º) on the flexural strength of medical-grade PMMA. Maximum and minimum flexural strength that be obtained in this work about 93 and 57 MPa respectively. The analysis of variance (ANOVA) results shows that the most effective factor is the layer height followed by infill density. The flexural strength rises significantly with decreases layer height and the skewing angle is in zero direction. The process parameters have been optimized through utilizing of genetic algorithms. The optimal results that emerged based on genetic algorithm technique are approximately 276 μm as layer height, 46% infill density, and skewing angle 89º, which maximize the flexural strength to 97 MPa at crossover for ten generation.

show abstract

Section: Genetic Algorithmmentioning

confidence: 99%